Aiming at problem of soft measuring modeling of complex chemical process with strong nonlinear feature

a method based on kernel extreme learning machine（KELM） and feature vector selection（FVS） is proposed. The input data is mapped to the high-dimensional space by kernel method

and a group of the largest unrelated data vector is selected as the feature space

and the original data is projected on the feature space to form new input data

and the soft measuring model is established based on KELM. KELM does not need to set the number of network hidden layer nodes

and the kernel function represents the unknown hidden layer nonlinear feature mapping

which has good generalization ability. In order to verify the effectiveness of the proposed method

it is applied to the soft measuring modeling of debutane tower. Under the same conditions

it is compared with the methods of KELM

KPCA-KELM

FVS-SVM

FVS-LR and other methods

the experimental results show that the FVS-KELM method has the highest correlation coefficient and modeling precision

and has advantages in chemical production.